Machine Learning Prescriptive Canvas for Optimizing Business Outcomes
It addresses the problem of inefficient decision-making in business data science projects for stakeholders like project and data science managers, though it appears incremental as it builds on existing prescriptive concepts.
The paper tackles the sub-optimality of predictive approaches in data science projects by advocating for a prescriptive framing, where models directly prescribe actions, and introduces the Prescriptive Canvas methodology to improve project framing and communication for better business impact.
Data science has the potential to improve business in a variety of verticals. While the lion's share of data science projects uses a predictive approach, to drive improvements these predictions should become decisions. However, such a two-step approach is not only sub-optimal but might even degrade performance and fail the project. The alternative is to follow a prescriptive framing, where actions are "first citizens" so that the model produces a policy that prescribes an action to take, rather than predicting an outcome. In this paper, we explain why the prescriptive approach is important and provide a step-by-step methodology: the Prescriptive Canvas. The latter aims to improve framing and communication across the project stakeholders including project and data science managers towards a successful business impact.